ACM Transactions on Graphics

Additive manufacturing has recently seen drastic improvements in resolution, making it now possible to fabricate features at scales of hundreds or even dozens of nanometers, which previously required very expensive lithographic methods. As a result, additive manufacturing now seems poised for optical applications, including those relevant to computer graphics, such as material design, as well as display and imaging applications. In this work, we explore the use of additive manufacturing for generating structural colors, where the structures are designed using a fabrication-aware optimization process. This requires a combination of full-wave simulation, a feasible parameterization of the design space, and a tailored optimization procedure. Many of these components should be re-usable for the design of other optical structures at this scale. We show initial results of material samples fabricated based on our designs. While these suffer from the prototype character of state-of-the-art fabrication hardware, we believe they clearly demonstrate the potential of additive nanofabrication for structural colors and other graphics applications

Joint CNN and Variational Model for Fully-automatic Image Colorization

International audienceThis paper aims to couple the powerful prediction of the convolutional neural network (CNN) to the accuracy at pixel scale of the variational methods. In this work, the limitations of the CNN-based image colorization approaches are described. We then focus on a CNN which is able to compute a statistical distribution of the colors for each pixel of the image based on a learning over a large color image database. After describing its limitation, the variational method of [17] is briefly recalled. This method is able to select a color candidate among a given set while performing a regularization of the result. By combining this approach with a CNN, we designed a fully automatic image coloriza-tion framework with an improved accuracy in comparison with CNN alone. Some numerical experiments demonstrate the increased accuracy performed by our method

Inertial Alternating Generalized Forward-Backward Splitting for Image Colorization

International audienceIn this paper, we propose a novel accelerated alternating optimization scheme to solve block-biconvex nonsmooth problems whose objectives can be split into smooth (separable) regularizers and simple coupling terms. The proposed method performs a Bregman distance based generalization of the well-known forward-backward splitting for each block, along with an inertial strategy which aims at getting empirical acceleration. We discuss the theoretical convergence of the proposed scheme and provide numerical experiments on image colorization

Motion parallax for 360° RGBD video

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today''s most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea